摘要
利用编码复杂度表示数据的结构稀疏度,通过降低编码复杂度实现结构稀疏.在稀疏表示分类模型的基础上,通过聚类排序的方法构造结构化字典,形成混合结构稀疏模型.此模型结合类间样本的定长组结构与类内样本的动态可重叠组结构,以及误差的标准稀疏结构.为实现混合结构稀疏重构,提出改进的混合结构贪婪算法.实验表明对数据字典进行聚类排序可有效改进人脸的识别性能,在相同条件下,混合结构的性能优于其他结构,文中算法也优于其他算法.
Coding complexity is utilized to represent the structural sparsity,and structural sparsity is achieved by means of reducing coding complexity. Based on the model of sparse representation classification,a structural dictionary is formed from clustering and sorting, sparsity model with mixed structure is constructed. This model combines fixed-length group structure between classes,and dynamic group structure within classes,as well as standard spare structure corresponding to error part. To reconstitute this mixed structural sparsity,an improved mixed structural greedy algorithm is proposed. Experimental results show that the clustering and sorting of the data dictionary can effectively improve the performance of face recognition. Under the same conditions,the performance of mixed structure is better than other structures,and the proposed algorithm outperforms other algorithms.
引文
[1]Wright J,Yang A Y,Ganesh A,et al.Robust Face Recognition via Sparse Representation.IEEE Trans on Pattern Analysis and Machine Intelligence,2009,31(2):210-227
[2]Yang A Y,Zhou Z H,Ma Y,et al.Towards a Robust Face Recognition System Using Compressive Sensing//Proc of the 11th Annual Conference of the International Speech Communication Association.Makuhari,Japan,2010:2250-2253
[3]Cai T J,Fan X P,Liu Z X.Dense Noise Face Recognition Based on Sparse Representation and Algorithm Optimization.Journal of Computer Applications,2012,32(8):2313-2315,2319(in Chinese)(蔡体健,樊晓平,刘遵雄.基于稀疏表示的高噪声人脸识别及算法优化.计算机应用,2012,32(8):2313-2315,2319)
[4]Zhang L,Yang M,Feng X C,et al.Collaborative Representation Based Classification for Face Recognition[EB/OL].[2014-06-30].http://arxiv.org/ftp/arxiv/papers/1204/1204.2358.pdf
[5]Deng W H,Hu J N,Guo J.Extended SRC:Undersampled Face Recognition via Intraclass Variant Dictionary.IEEE Trans on Pattern Analysis and Machine Intelligence,2012,34(9):1864-1870
[6]Majumdar A,Ward R K.Classification via Group Sparsity Promoting Regularization//Proc of the IEEE International Conference on Acoustics,Speech and Signal Processing.Taibei,China,2009:861-864
[7]Elhamifar E,Vidal R.Robust Classification Using Structured Sparse Representation//Proc of the IEEE Conference on Computer Vision and Pattern Recognition.Providence,USA,2011:1873-1879
[8]Huang J Z,Zhang T.The Benefit of Group Sparsity.The Annals of Statistics,2010,38(4):1978-2004
[9]Zhang X,Pharn D S,Liu W Q,et al.Optimal Metric Selection for Improved Multi-pose Face Recognition with Group Information//Proc of the 21st International Conference on Pattern Recognition.Tsukuba,Japan,2012:1675-1678
[10]Hu Z P,Song S F.Sparse Representation Algorithm for Image Recognition Based on the Combination of Structured Sparse and Atom Sparse.Journal of Signal Processing,2013,29(7):888-895(in Chinese)(胡正平,宋淑芬.原子稀疏结合块结构稀疏的联合表示图像识别算法.信号处理,2013,29(7):888-895)
[11]Jenatton R,Obozinski G,Bach F.Structured Sparse Principal Component Analysis//Proc of the 13th International Conference on Artificial Intelligence and Statistics.Sardinia,Italy,2010:366-373
[12]Bach F,Jenatton R,Mairal J,et al.Structured Sparsity through Convex Optimization.Statistical Science,2011,27(4):450-468
[13]Mansinghka V K,Kemp C,Tenenbaum J B,et al.Structured Priors for Structure Learning[EB/OL].[2014-06-30].http://arxiv.org/ftp/arxiv/papers/1206/1206.6852.pdf
[14]Scott S L,Varian H.Predicting the Present with Bayesian Structural Time Series.International Journal of Mathematical Modelling and Numerical Optimisation,2014,5(1/2):4-23
[15]Chen J,Bushman F D,Lewis J D,et al.Structure-Constrained Sparse Canonical Correlation Analysis with an Application to Microbiome Data Analysis.Biostatistics,2013,14(2):244-258
[16]Hu Z P,Zhao S H,Li J.Robust Occlusion Pattern Recognition Algorithm Based on Block Sparse Recursive Residuals Analysis.Pattern Recognition and Artificial Intelligence,2014,27(1):70-76(in Chinese)(胡正平,赵淑欢,李静.基于块稀疏递推残差分析的稀疏表示遮挡鲁棒识别算法研究.模式识别与人工智能,2014,27(1):70-76)
[17]Huang J Z,Zhang T,Metaxas D.Learning with Structured Sparsity.Journal of Machine Learning Research,2011,12:3371-3412
[18]Martinez A M,Benavente R.The AR Face Database.CVC Technical Report,24.Barcelona,Spain:Universitat Autònoma de Barcelona,1998
[19]Georghiades A S,Belhumeur P N,Kriegman D J.From Few to Many:Illumination Cone Models for Face Recognition under Variable Lighting and Pose.IEEE Trans on Pattern Analysis and Machine Intelligence,2001,23(6):643-660
[20]Yang A Y,Sastry S S,Ganesh A,et al.Fast l1-Minimization Algorithms and an Application in Robust Face Recognition:A Review//Proc of the 17th IEEE International Conference on Image Processing.Hong Kong,China,2010:1849-1852
[21]Cai T J,Fan X P.Analysis and Optimization of Greedy Pursuit Algorithms.Journal of Chinese Computer Systems,2014,35(5):1116-1119(in Chinese)(蔡体健,樊晓平.贪婪追踪系列算法的分析与优化.小型微型计算机系统,2014,35(5):1116-1119)
